Seamless prediction of high-impact weather events: a comparison of actionable forecasts

TL;DR

This paper introduces a new high-impact weather forecast index derived from recent verification methods, compares it with existing indices, and demonstrates its superior discrimination power for actionable weather predictions across various lead times.

Contribution

The study presents a novel forecast index for high-impact weather, validated against established indices, enhancing the ability to produce seamless, actionable forecasts over different time horizons.

Findings

The new index shows the strongest discrimination power among tested indices.

It performs particularly well at longer lead times.

Forecast discretisation improves communication and decision-making.

Abstract

A new index for high-impact weather forecasting is introduced and assessed in comparison with the well-established extreme forecast index (EFI). Two other ensemble summary statistics are also included in this comparison study: the shift-of-tail and a standardised ensemble mean anomaly. All these forecasts are based on the same ingredients: the ensemble forecast run at the European Centre for Medium-Range Weather Forecasts and the corresponding model climatology derived from a set of reforecasts. The new index emerges from recent developments in forecast verification of extreme events: it is derived as a consistent forecast with the diagonal score, a weighted version of the continuous ranked probability score targetting high-impact events. In this study, we emphasise the importance of forecast discretisation for communication purposes and decision-making. A forecast is actionable in the situation where a user can decide to take action when a threshold is exceeded by the forecast. Forecast verification is performed to assess both the potential skill of the different indices as well as their specific skill as actionable forecasts. Among the investigated actionable forecasts, the new proposed index demonstrates the strongest discrimination power, in particular at longer lead times, paving the way for seamless predictions of high-impact weather across time ranges.